提交 3048b1fb 编写于 作者: T Tao Luo 提交者: GitHub

Merge pull request #1027 from lcy-seso/enable_drop_in_average_and_max_layer

Enable drop in average and max layer
......@@ -2301,14 +2301,9 @@ def Generator(
@config_layer('expand')
class ExpandLayer(LayerBase):
def __init__(self,
name,
inputs,
trans_type='non-seq',
device=None,
bias=False):
def __init__(self, name, inputs, trans_type='non-seq', bias=False, **xargs):
super(ExpandLayer, self).__init__(
name, 'expand', 0, inputs=inputs, device=device)
name, 'expand', 0, inputs=inputs, **xargs)
config_assert(
len(self.inputs) == 2, 'ExpandLayer takes 2 and only 2 inputs')
self.config.trans_type = trans_type
......@@ -2339,11 +2334,10 @@ class MaxLayer(LayerBase):
inputs,
trans_type='non-seq',
active_type='linear',
device=None,
bias=False,
output_max_index=None):
super(MaxLayer, self).__init__(
name, 'max', 0, inputs=inputs, device=device)
output_max_index=None,
**xargs):
super(MaxLayer, self).__init__(name, 'max', 0, inputs=inputs, **xargs)
config_assert(len(self.inputs) == 1, 'MaxLayer must have 1 input')
self.config.trans_type = trans_type
self.config.active_type = active_type
......@@ -2390,15 +2384,15 @@ class SequenceLastInstanceLayer(LayerBase):
inputs,
active_type='linear',
trans_type='non-seq',
device=None,
bias=False):
bias=False,
**xargs):
super(SequenceLastInstanceLayer, self).__init__(
name,
'seqlastins',
0,
inputs=inputs,
device=device,
active_type=active_type)
active_type=active_type,
**xargs)
config_assert(
len(inputs) == 1, 'SequenceLastInstanceLayer must have 1 input')
self.config.trans_type = trans_type
......@@ -2410,39 +2404,29 @@ class SequenceLastInstanceLayer(LayerBase):
@config_layer('seqfirstins')
class SequenceFirstInstanceLayer(SequenceLastInstanceLayer):
def __init__(
self,
name,
inputs,
active_type='linear',
trans_type='non-seq',
device=None,
bias=False, ):
def __init__(self,
name,
inputs,
active_type='linear',
trans_type='non-seq',
bias=False,
**xargs):
super(SequenceFirstInstanceLayer, self).__init__(
name,
inputs=inputs,
active_type=active_type,
device=device,
bias=bias)
name, inputs=inputs, active_type=active_type, bias=bias, **xargs)
self.config.trans_type = trans_type
self.config.select_first = True
@config_layer('seqconcat')
class SequenceConcatLayer(LayerBase):
def __init__(self,
name,
inputs,
active_type='linear',
device=None,
bias=False):
def __init__(self, name, inputs, active_type='linear', bias=False, **xargs):
super(SequenceConcatLayer, self).__init__(
name,
'seqconcat',
0,
inputs=inputs,
device=device,
active_type=active_type)
active_type=active_type,
**xargs)
config_assert(
len(inputs) == 2, 'SequenceConcatLayer must have 2 inputs')
for input_index in xrange(len(self.inputs)):
......@@ -2458,15 +2442,15 @@ class SequenceReshapeLayer(LayerBase):
size,
inputs,
active_type='linear',
device=None,
bias=False):
bias=False,
**xargs):
super(SequenceReshapeLayer, self).__init__(
name,
'seqreshape',
size,
inputs=inputs,
device=device,
active_type=active_type)
active_type=active_type,
**xargs)
config_assert(
len(inputs) == 1, 'SequenceReshapeLayer must have 1 inputs')
self.set_layer_size(size)
......@@ -2475,19 +2459,9 @@ class SequenceReshapeLayer(LayerBase):
@config_layer('subseq')
class SubSequenceLayer(LayerBase):
def __init__(self,
name,
inputs,
active_type='linear',
device=None,
bias=False):
def __init__(self, name, inputs, active_type='linear', bias=False, **xargs):
super(SubSequenceLayer, self).__init__(
name,
'subseq',
0,
inputs=inputs,
device=device,
active_type=active_type)
name, 'subseq', 0, inputs=inputs, active_type=active_type, **xargs)
config_assert(len(inputs) == 3, 'SubSequenceLayer must have 3 inputs')
input_layer0 = self.get_input_layer(0)
size = input_layer0.size
......@@ -2644,15 +2618,10 @@ class AverageLayer(LayerBase):
average_strategy='average',
trans_type='non-seq',
active_type='linear',
device=None,
bias=False):
bias=False,
**xargs):
super(AverageLayer, self).__init__(
name,
'average',
0,
inputs=inputs,
device=device,
active_type=active_type)
name, 'average', 0, inputs=inputs, active_type=active_type, **xargs)
self.config.average_strategy = average_strategy
self.config.trans_type = trans_type
config_assert(len(inputs) == 1, 'AverageLayer must have 1 input')
......@@ -2676,9 +2645,9 @@ class CosSimLayer(LayerBase):
@config_layer('tensor')
class TensorLayer(LayerBase):
def __init__(self, name, size, inputs, device=None, bias=True, **xargs):
def __init__(self, name, size, inputs, bias=True, **xargs):
super(TensorLayer, self).__init__(
name, 'tensor', size, inputs=inputs, device=device, **xargs)
name, 'tensor', size, inputs=inputs, **xargs)
config_assert(len(self.inputs) == 2, 'TensorLayer must have 2 inputs')
config_assert(size > 0, 'size must be positive')
config_assert(inputs[1].parameter_name == None,
......
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